Search Results (153)

This study examines urban expansion, coastal erosion, and extreme wave events in Licantén, Maule Region, following the 2010 earthquake and tsunami. Using multi-source data—Landsat and Sentinel-2 imagery, ERA5 reanalysis, high-resolution Maxar images, UAV surveys, and the CoastSat algorithm—we detected significant urban growth in tsunami-prone areas: Iloca (36.88%), La Pesca (33.34%), and Pichibudi (20.78%). A 39-year shoreline reconstruction (1985–2024) revealed notable changes in erosion rates and shoreline dynamics using DSAS v6.0, influenced by tides, storm surges, and wave action modeled in R to quantify storm surge events over time. Results underscore the lack of urban planning in hazard-exposed areas and the urgent need for resilient coastal management under climate change. Full article

The primary contribution of this study lies in analyzing the dynamic drivers during two anomalous sea level rise events in the Bohai Sea through coupled numeric modeling using the Weather Research and Forecasting (WRF) model and the Finite-Volume Community Ocean Model (FVCOM) integrated with the Simulating Waves Nearshore (SWAN) module (hereafter referred to as FVCOM-SWAVE). WRF-derived wind speeds (0.05° grid resolution) were validated against Haiyang-2 (HY-2) scatterometer observations, yielding a root mean square error (RMSE) of 1.88 m/s and a correlation coefficient (Cor) of 0.85. Similarly, comparisons of significant wave height (SWH) simulated by FVCOM-SWAVE (0.05° triangular mesh) with HY-2 altimeter data showed an RMSE of 0.67 m and a Cor of 0.84. Four FVCOM sensitivity experiments were conducted to assess drivers of sea level rise, validated against tide gauge observations. The results identified tides as the primary driver of sea level rise, with wind stress and elevation forcing (e.g., storm surge) amplifying variability, while currents exhibited negligible influence. During the two events, i.e., 20–21 October and 25–26 August 2024, elevation forcing contributed to localized sea level rises of 0.6 m in the northern and southern Bohai Sea and 1.1 m in the southern Bohai Sea. A 1 m surge in the northern region correlated with intense Yellow Sea winds (20 m/s) and waves (5 m SWH), which drove water masses into the Bohai Sea. Stokes transport (wave-driven circulation) significantly amplified water levels during the 21 October and 26 August peak, underscoring critical wave–tide interactions. This study highlights the necessity of incorporating tides, wind, elevation forcing, and wave effects into coastal hydrodynamic models to improve predictions of extreme sea level rise events. In contrast, the role of imposed boundary current can be marginalized in such scenarios. Full article

California and most other coastlines around the nation and the world are being impacted by both long-term sea-level rise (SLR) and short-term extreme events. Global sea level over the last 10 years of satellite altimetry has averaged approximately 4.1 mm/yr. (~16 in./100 yrs.), although this rate is accelerating at about 1.2 mm/yr. per decade. Projections of future sea levels have now been developed by many different agencies, organizations, and committees, and cluster around 12 inches by 2050. Over the near term, however, until mid-century, and likely beyond, it will be the short-term extreme events such as hurricanes along the U.S. Atlantic and Gulf coasts, and the coincidence of very large waves and high astronomic tides along the U.S. Pacific coasts that will pose the major threat to both public infrastructure and private development. Full article

Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. TiDE compresses historical time series and covariates into latent representations via residual connections and reconstructs future values through a temporal decoder, capturing both long- and short-term dependencies. We trained the model using data from 2020 to 2022 from Australia’s Desert Knowledge Australia Solar Centre (DKASC), with 2023 data used for testing. Forecast accuracy was evaluated using the $R^2$ coefficient of determination, mean absolute error (MAE), and root mean square error (RMSE). In the 5 min ahead forecasting test, TiDE demonstrated high short-term accuracy with an $R^2$ of 0.952, MAE of 0.150, and RMSE of 0.349, though performance declines for longer horizons, such as the 1 h ahead forecast, compared to other algorithms. For one-day-ahead forecasts, it achieved an $R^2$ of 0.712, an MAE of 0.507, and an RMSE of 0.856, effectively capturing medium-term weather trends but showing limited responsiveness to sudden weather changes. Further analysis indicated improved performance in cloudy and rainy weather, and seasonal analysis reveals higher accuracy in spring and autumn, with reduced accuracy in summer and winter due to extreme conditions. Additionally, we explore the TiDE model’s sensitivity to input environmental variables, algorithmic versatility, and the implications of forecasting errors on PV grid integration. These findings highlight TiDE’s superior forecasting accuracy and robust adaptability across weather conditions, while also revealing its limitations under abrupt changes. Full article

Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters. Full article

This study analyzes the 96 h flow velocity time series data from the Zhoushan Sea during Typhoon In−fa to investigate the conditions for extreme flow velocities. Through force analysis of the unit fluid and statistical analysis of topographic features, we identified the critical water depth, slope, and sea surface width for extreme flow velocities under ideal conditions as 15 m, 4.5°, and 2000 m, respectively. The Submarine Topographic Characteristic Index (STCI) is introduced for the first time in this study, revealing its significant impact on extreme flow velocities. Three types of “extreme velocity points”—associated with constant storm surge, astronomical tide, and typhoon storm surge—were defined, occurring over 85% of the time during typhoon events. These extreme velocity points were analyzed in relation to their topographic characteristics, including water depth, slope, and sea surface width. Simulations of Typhoon In−fa in the Zhoushan Sea area were used to construct the STCI model, resulting in the following weightings: water depth = 0.96, slope = 0.39, and sea surface width = 0.49. Typhoon In−fa occurred in 2021, exhibited a maximum wind speed of approximately 35 m/s, and played a key role in the hydrodynamic processes investigated in this study. Validation with Typhoons Muifa (2021) and Bebinca (2413) confirmed the model’s high consistency. The STCI model provides insight into the occurrence of extreme velocities, categorizing them according to tidal phase and typhoon influence. Preliminary findings indicate the model’s applicability under varying typhoon intensities, offering a robust tool for predicting extreme seabed flow velocities during typhoon events. Full article

This study advances coastal flood risk assessment by incorporating El Niño–Southern Oscillation (ENSO) phase information into the Joint Probability of Maxima Method (ENSO-JPMM) for extreme water level prediction in Annapolis, MD. Using data from GLOSS/Extended Sea 135 Level Analysis Version 3 (GESLA-3) dataset and water level records from 1950–2021, we demonstrate that ENSO phases significantly affects flood risk probabilities through their influence on mean sea level, astronomical tides, and skew surge components. We introduce an enhanced JPMM framework that employs phase-specific scaling factors and vertical offsets derived from historical observations, with El Niño conditions associated with higher mean water levels (0.433 m) compared to La Niña (0.403 m) and Neutral phases (0.409 m). The ENSO-JPMM demonstrates improved predictive accuracy across all phases, with root mean square error reductions of up to 5.96% during Neutral conditions and 3.56% during El Niño phases. By implementing a detailed methodology for mean sea level estimation and skew surge analysis, our approach provides a more detailed framework for separating tidal and non-tidal components while accounting for climate variability. The results indicate that traditional extreme value analyses may underestimate flood risks by failing to account for ENSO-driven variability, which can modulate mean water levels by up to 3.0 cm in Annapolis. This research provides insight for coastal infrastructure planning and flood risk management, particularly as climate change potentially alters ENSO characteristics and their influence on extreme water levels. The methodology presented here, while specific to Annapolis MD, can be adapted for other coastal regions to improve flood risk assessments and enhance community resilience planning. Full article

Surf during typhoon events poses severe threats to coastal infrastructure and public safety. Traditional monitoring approaches, including in situ sensors and numerical simulations, face inherent limitations in capturing surf impacts—sensors are constrained by point-based measurements, while simulations require intensive computational resources for real-time monitoring. Video-based monitoring offers promising potential for continuous surf observation, yet the development of deep learning models for surf detection remains underexplored, primarily due to the lack of high-quality training datasets from typhoon events. To bridge this gap, we propose a lightweight YOLO (You Only Look Once) based framework for real-time surf detection. A novel dataset of 2855 labeled images with surf annotations, collected from five typhoon events at the Chongwu Tide Gauge Station, captures diverse scenarios such as daytime, nighttime, and extreme weather conditions. The proposed YOLOv6n model achieved 99.3% $mA{P}_{50}$ at 161.8 FPS, outperforming both other YOLO variants and traditional two-stage detectors in accuracy and computational efficiency. Scaling analysis further revealed that YOLO models with 2–5 M parameters provide an optimal trade-off between accuracy and computational efficiency. These findings demonstrate the effectiveness of YOLO-based video monitoring systems for real-time surf detection, offering a practical and reliable solution for coastal hazard monitoring under extreme weather conditions. Full article

The historical City of Venice, with its lagoon, has been severely exposed to repeated marine flooding since historical times due to the combined effects of sea level rise (SLR) and land subsidence (LS) by natural and anthropogenic causes. Although the sea level change in this area has been studied for several years, no detailed flooding scenarios have yet been realized to predict the effects of the expected SLR in the coming decades on the coasts and islands of the lagoon due to global warming. From the analysis of geodetic data and climatic projections for the Shared Socioeconomic Pathways (SSP1-2.6; SSP3-7.0 and SSP5-8.5) released in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), we estimated the rates of LS, the projected local relative sea level rise (RSLR), and the expected extent of flooded surfaces for 11 selected areas of the Venice Lagoon for the years 2050, 2100, and 2150 AD. Vertical Land Movements (VLM) were obtained from the integrated analysis of Global Navigation Satellite System (GNSS) and Interferometry Synthetic Aperture Radar (InSAR) data in the time spans of 1996–2023 and 2017–2023, respectively. The spatial distribution of VLM at 1–3 mm/yr, with maximum values up to 7 mm/yr, is driving the observed variable trend in the RSLR across the lagoon, as also shown by the analysis of the tide gauge data. This is leading to different expected flooding scenarios in the emerging sectors of the investigated area. Scenarios were projected on accurate high-resolution Digital Surface Models (DSMs) derived from LiDAR data. By 2150, over 112 km² is at risk of flooding for the SSP1-2.6 low-emission scenario, with critical values of 139 km² for the SSP5-8.5 high-emission scenario. In the case of extreme events of high water levels caused by the joint effects of astronomical tides, seiches, and atmospheric forcing, the RSLR in 2150 may temporarily increase up to 3.47 m above the reference level of the Punta della Salute tide gauge station. This results in up to 65% of land flooding. This extreme scenario poses the question of the future durability and effectiveness of the MoSE (Modulo Sperimentale Elettromeccanico), an artificial barrier that protects the lagoon from high tides, SLR, flooding, and storm surges up to 3 m, which could be submerged by the sea around 2100 AD as a consequence of global warming. Finally, the expected scenarios highlight the need for the local communities to improve the flood resiliency plans to mitigate the consequences of the expected RSLR by 2150 in the UNESCO site of Venice and the unique environmental area of its lagoon. Full article

In order to examine the seasonal variations in the morphological characteristics and diet of Mactra veneriformis in the Northern Yellow River Delta’s intertidal zone and provide a scientific basis for its resource conservation and population restoration, tested clams were collected in four consecutive seasons from summer of 2022 to spring of 2023. Morphological traits were measured, and the DNA of the stomach contents was analyzed using high-throughput sequencing. The tidal differences and seasonal variations in the northern habitat of the Yellow River Estuary significantly affect the morphological characteristics and growth of M. veneriformis. Among the four seasons, significant differences in the morphological characteristics of M. veneriformis were observed between the middle-tide and low-tide zones (p < 0.05). In both middle-tide and low-tide zones, the morphological characteristics and body wet weight of M. veneriformis in winter were significantly higher than those in other seasons (p < 0.05). Moreover, the morphological characteristics of M. veneriformis were extremely significantly influenced by the interaction between tide and season (two-way interaction, p < 0.001). In all seasons, M. veneriformis in the middle- and low-tide zones exhibited positive allometric growth. While there was no significant difference in the stomach content between the spring and summer samples in the same tidal zone (p > 0.05), there was a significant difference between the middle- and low-tide samples in winter (p < 0.05). This suggests that seasonal variations, rather than tidal differences, had a larger impact on the diet of M. veneriformis in the northern Yellow River estuary and that feeding differences may be related to changes in environmental factors, such as temperature. The findings of this study provide initial insights into the feeding ecology of M. veneriformis and offer a scientific foundation for the conservation and management of its resources. Full article

Storm surge barriers are crucial for the flood protection of the Netherlands and other deltas. In the Netherlands, the reliability of flood defenses is typically assessed based on extreme water levels and wave height statistics. Yet, in the case of operated flood defenses, such as storm surge barriers, the temporal clustering of successive events may be just as important. This study investigates the evolution and associated flood risk of clusters of successive storm tide peaks at the Maeslant Storm Surge Barrier in the Netherlands. Two mechanisms are considered. Multi-peak storm surge events, as a consequence of tidal movement on top of the surge, are studied by means of stochastic storm tide events. Clusters of storm tides resulting from different, but related storms are investigated by means of time series analysis of a long sea-level record. We conclude that the tendency of extreme storm tide peaks to cluster is especially related to the seasonality in storm activity. In the current situation, the occurrence of clusters of storm tide peaks have only a minor influence of the flood risk in the area behind the Maeslant Storm Surge Barrier. We envision, however, that this influence is likely to increase with sea-level rise. The numbers are, however, uncertain due to the strong sensitivity to assumptions, model choices and the applied data set. More insight into the statistics of the time evolution of extreme sea water levels is needed to better understand and ultimately to reduce these uncertainties. Full article

A global database of coastal flooding impacts resulting from extreme sea levels is developed for the present day and for the years 2050 and 2100. The database consists of three sub-datasets: the extreme sea levels, the coastal areas flooded by these extreme sea levels, and the resulting socioeconomic implications. The extreme sea levels consider the processes of storm surge, tide levels, breaking wave setup and relative sea level rise. The socioeconomic implications are expressed in terms of Expected Annual Population Affected (EAPA) and Expected Annual Damage (EAD), and presented at the global, regional and national scales. The EAPA and EAD are determined both for existing coastal defence levels and assuming two plausible adaptation scenarios, along with socioeconomic development narratives. All the sub-datasets can be visualized with a Digital Twin platform based on a GIS-based mapping host. This publicly available database provides a first-pass assessment, enabling users to extract and identify global and national coastal hotspots under different projections of sea level rise and socioeconomic developments. Full article

The complexity and uncertainty of the marine environment pose significant challenges to the stability and coverage of communication links. Due to the limited coverage range of traditional onshore base stations (BSs) in marine environments, relay technology has become an essential approach to extending communication coverage. However, the rapid variations in marine wireless channels and the complexity of hydrological conditions make it extremely difficult to obtain accurate channel state information (CSI). In particular, dynamic environmental factors such as waves, tides and wind speed cause channel parameters to fluctuate significantly over time. To address these challenges, this paper proposes a cooperative communication strategy based on ships and designs a novel channel modeling method to accurately capture the characteristics of marine wireless channels. Furthermore, a deep learning-based optimization scheme is proposed, which formulates the relay selection problem as a spatiotemporal classification task. By integrating the spatial positions of candidate relays and their communication conditions, the proposed scheme enables real-time selection of the optimal relay while evaluating link connectivity probabilities under hydrological influences. Simulation results confirm that the proposed method achieves high accuracy even in rapidly changing marine environments. Full article

The aim of this study was to analyze the application of the probability of extreme water level predictions along the entire Baltic Sea coast. In the initial part of this work, the critical sea levels off the Baltic States were reviewed. These levels are related to the height of the breakwaters and were determined on the basis of probabilistic methods. Then, the heights of the theoretical water levels in the entire quantile range were determined. Calculations were performed using Gumbel and Pearson III type distributions. Visualizations of the theoretical maximum and minimum water levels, as well as calculations related to the sea surface and length of the coastline, were made using ArcGIS 10.2.1 software. A comparison of theoretical water levels from two periods showed that over the last 60 years, there has been a stable trend of an increase in both the theoretical and observed maximum water levels of 2.6 mm/year. At the same time, the return period for the Baltic tide gauge stations was reduced by an average of about 50%. It could thus be concluded that hydrological hazards in the Baltic Sea region appeared twice as often as they did in the first half of the 20th century. Later in this work, we determined what size of the sea surface and the coastline length corresponded to particular sea level ranges for different return periods. For the maximum theoretical water with a 200-year return period, as much as 19.1% of the Baltic Sea surface and 23.8% of its coastline length may be influenced by extremely high sea levels (≥200 cm). These are areas in the inner parts of the great Baltic gulfs. For them, critical water levels are lower than 200 cm, which indicates a potential risk of storm floods. Based on probability calculations, it could be concluded that Pärnu Bay, within which lies the Pärnu tide gauge station, is the most hydrologically dangerous basin in the Baltic Sea. Full article

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m². The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales. Full article